Recruiting from the Network: Discovering Twitter Users Who Can Help Combat Zika Epidemics

Missier, Paolo; McClean, Callum; Carlton, Jonathan; Cedrim, Diego; Silva, Leonardo; Garcia, Alessandro; Plastino, Alexandre; Romanovsky, Alexander

doi:10.1007/978-3-319-60131-1_30

Cited by 15 publications

(12 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This means that for the majority of users the number of COVID-19 posts follows the increasing of the number of total posts. Missier et al [48] showed that social media analytics can be used to pinpoint individuals who are actively contributing to social discourse on the specific topic of the Zika virus and its consequences, and are thus likely to be sensitive to health promotion campaigns, by focusing on Twitter content related to the Zika virus and its effect on people. For our case, also, we find some users who can be qualified as COVID-19 publish engine despite that their accounts are for persons.…”

Section: Facebook Users Covid-19 Related Behaviourmentioning

confidence: 99%

Multilingual topic modeling for tracking COVID-19 trends based on Facebook data analysis

Taieb

Aouicha

2021

Appl Intell

View full text Add to dashboard Cite

Social data has shown important role in tracking, monitoring and risk management of disasters. Indeed, several works focused on the benefits of social data analysis for the healthcare practices and curing domain. Similarly, these data are exploited now for tracking the COVID-19 pandemic but the majority of works exploited Twitter as source. In this paper, we choose to exploit Facebook, rarely used, for tracking the evolution of COVID-19 related trends. In fact, a multilingual dataset covering 7 languages (English (EN), Arabic (AR), Spanish (ES), Italian (IT), German (DE), French (FR) and Japanese (JP)) is extracted from Facebook public posts. The proposal is an analytics process including a data gathering step, pre-processing, LDA-based topic modeling and presentation module using graph structure. Data analysing covers the duration spanned from January 1st, 2020 to May 15, 2020 divided on three periods in cumulative way: first period January-February, second period March-April and the last one to 15 May. The results showed that the extracted topics correspond to the chronological development of what has been circulated around the pandemic and the measures that have been taken according to the various languages under discussion representing several countries.

show abstract

Section: Facebook Users Covid-19 Related Behaviourmentioning

confidence: 99%

Multilingual topic modeling for tracking COVID-19 trends based on Facebook data analysis

Taieb

Aouicha

2021

Appl Intell

View full text Add to dashboard Cite

show abstract

“…This means that for the majority of users the number of COVID-19 posts follows the increasing of the number of total posts. Missier et al (2017) showed that social media analytics can be used to pinpoint individuals who are actively contributing to social discourse on the specific topic of the Zika virus and its consequences, and are thus likely to be sensitive to health promotion campaigns, by focusing on Twitter content related to the Zika virus and its effect on people. For our case, also, we find some users who can be qualified as COVID-19 publish engine despite that their accounts are for persons.…”

Section: Facebook Users Covid-19 Related Behaviourmentioning

confidence: 99%

Multilingual topic modelling for tracking COVID-19 trends based on Facebook data analysis

Amara

Aouicha

2020

Preprint

View full text Add to dashboard Cite

Social data has shown important role in tracking, monitoring and risk management of disasters. Indeed, several works focused on the benets of social data analysis to the healthcare practices and curing. Similarly, these data are exploited now for tracking the COVID-19 pandemic but the majority of works exploited twitter as source. In this paper, we choose to exploit Facebook, rarely used, for tracking the evolution of COVID-19 related trends. In fact, a multilingual dataset covering 7 languages (English (EN), Arabic (AR), Spanish (ES), Italian (IT), German (DE), French (FR) and Japanese (JP)) is extracted from Facebook public posts. The proposal is an analytics process including a data gathering step, pre-processing, LDA-based topic modelling and presentation module using graph structure. Data analysing covers the duration spanned from January 1st, 2020 to May 15, 2020 divided on three periods in cumulative way: rst period January-February, second period March-April and the last one to 15 May. The results showed that the extracted topics correspond to the chronological development of what has been circulated around the pandemic and the measures that have been taken in the various languages under discussion.

show abstract

“…Help from community activists is badly needed to supplement the scarce public resources deployed on the ground, and efforts have been recorded to document how technology can be brought to bear for this [3,29]. Our own work in this setting has so far focused on identifying relevant content on Twitter that may point health authorities directly to mosquito breeding sites [31], as well as to users who have shown interest in those topics, i.e., by posting relevant content on Twitter [20]. In this work we generalise such efforts, with an aim to develop techniques for the semi-automatic detection of online activists on Twitter.…”

Section: Introductionmentioning

confidence: 99%

A customisable pipeline for the semi-automated discovery of online activists and social campaigns on Twitter

et al. 2021

Self Cite

View full text Add to dashboard Cite

Substantial research is available on detecting influencers on social media platforms. In contrast, comparatively few studies exists on the role of online activists, defined informally as users who actively participate in socially-minded online campaigns. Automatically discovering activists who can potentially be approached by organisations that promote social campaigns is important, but not easy, as they are typically active only locally, and, unlike influencers, they are not central to large social media networks. We make the hypothesis that such interesting users can be found on Twitter within temporally and spatially localised contexts. We define these as small but topical fragments of the network, containing interactions about social events or campaigns with a significant online footprint. To explore this hypothesis, we have designed an iterative discovery pipeline consisting of two alternating phases of user discovery and context discovery. Multiple iterations of the pipeline result in a growing dataset of user profiles for activists, as well as growing set of online social contexts. This mode of exploration differs significantly from prior techniques that focus on influencers, and presents unique challenges because of the weak online signal available to detect activists. The paper describes the design and implementation of the pipeline as a customisable software framework, where user-defined operational definitions of online activism can be explored. We present an empirical evaluation on two extensive case studies, one concerning healthcare-related campaigns in the UK during 2018, the other related to online activism in Italy during the COVID-19 pandemic.

show abstract

Recruiting from the Network: Discovering Twitter Users Who Can Help Combat Zika Epidemics

Cited by 15 publications

References 14 publications

Multilingual topic modeling for tracking COVID-19 trends based on Facebook data analysis

Multilingual topic modeling for tracking COVID-19 trends based on Facebook data analysis

Multilingual topic modelling for tracking COVID-19 trends based on Facebook data analysis

A customisable pipeline for the semi-automated discovery of online activists and social campaigns on Twitter

Contact Info

Product

Resources

About